Chapter 4 Compositional analysis

load("data/data_podarcis_filfolensis.Rdata")
load("data/data_podarcis_gaigeae.Rdata")
load("data/data_podarcis_milensis.Rdata")
load("data/data_podarcis_pityusensis.Rdata")
load("data/data_podarcis_all.Rdata")

4.1 Taxonomy barplots

4.1.1 Podarcis filfolensis

genome_counts_filt_pf %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata_pf, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata_pf, by = join_by(sample == sample)) %>% #append sample metadata
  filter(!is.na(count)) %>%
  ggplot(aes(y=count,x=sample, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors_pf) +
    labs(x = "Relative abundance", y ="Samples") +
    facet_nested(. ~ population,  scales="free", space="free") + #facet per day and treatment
    scale_y_continuous(expand = c(0.001, 0.001)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          axis.title.x = element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black"),
          legend.position = "none",
          strip.background.x=element_rect(color = NA, fill= "#f4f4f4"))

genome_counts_filt_pg %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata_pg, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata_pg, by = join_by(sample == sample)) %>% #append sample metadata
  filter(!is.na(count)) %>%
  ggplot(aes(y=count,x=sample, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors_pg) +
    labs(x = "Relative abundance", y ="Samples") +
    facet_nested(. ~ population,  scales="free", space="free") + #facet per day and treatment
    scale_y_continuous(expand = c(0.001, 0.001)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          axis.title.x = element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black"),
          legend.position = "none",
          strip.background.x=element_rect(color = NA, fill= "#f4f4f4"))

genome_counts_filt_pm %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata_pm, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata_pm, by = join_by(sample == sample)) %>% #append sample metadata
  filter(!is.na(count)) %>%
  ggplot(aes(y=count,x=sample, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors_pm) +
    labs(x = "Relative abundance", y ="Samples") +
    facet_nested(. ~ population,  scales="free", space="free") + #facet per day and treatment
    scale_y_continuous(expand = c(0.001, 0.001)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          axis.title.x = element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black"),
          legend.position = "none",
          strip.background.x=element_rect(color = NA, fill= "#f4f4f4"))

genome_counts_filt_pp %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata_pp, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata_pp, by = join_by(sample == sample)) %>% #append sample metadata
  filter(!is.na(count)) %>%
  ggplot(aes(y=count,x=sample, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors_pp) +
    labs(x = "Relative abundance", y ="Samples") +
    facet_nested(. ~ population,  scales="free", space="free") + #facet per day and treatment
    scale_y_continuous(expand = c(0.001, 0.001)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          axis.title.x = element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black"),
          legend.position = "none",
          strip.background.x=element_rect(color = NA, fill= "#f4f4f4"))

4.2 Taxonomic representation

4.2.1 Phylum

phylum_summary <- genome_counts_filt_all %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata_all, by = join_by(sample == sample)) %>% #append sample metadata
  left_join(genome_metadata_all, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,phylum) %>%
  summarise(relabun=sum(count))

phylum_summary %>%
    filter(!is.na(relabun)) %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun),sd=sd(relabun)) %>%
    mutate(phylum= sub("^p__", "", phylum)) %>%
    arrange(-mean) %>%
    tt()
phylum mean sd
Bacillota_A 4.355073e-01 0.1637303694
Bacteroidota 3.795311e-01 0.1493442024
Bacillota 6.693929e-02 0.0613238855
Pseudomonadota 4.237993e-02 0.0659965403
Desulfobacterota 1.762937e-02 0.0168493333
Campylobacterota 1.394838e-02 0.0229133933
Cyanobacteriota 8.474584e-03 0.0120549930
Verrucomicrobiota 8.226209e-03 0.0140684507
Bacillota_C 6.505350e-03 0.0077147585
Spirochaetota 5.445050e-03 0.0249843346
Bacillota_B 4.111308e-03 0.0057340543
Fusobacteriota 3.024136e-03 0.0134489815
Halobacteriota 2.947999e-03 0.0105666272
Actinomycetota 2.221294e-03 0.0190806008
Chlamydiota 1.109162e-03 0.0112564276
Elusimicrobiota 1.104802e-03 0.0036746035
Synergistota 3.666072e-04 0.0024311729
Planctomycetota 1.908305e-04 0.0017144286
1.473864e-04 0.0009299590
Deferribacterota 1.413909e-04 0.0006340007
Thermoplasmatota 4.851518e-05 0.0006362706
phylum_arrange <- phylum_summary %>%
    filter(!is.na(relabun)) %>%
    group_by(phylum) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    #mutate(phylum= sub("^p__", "", phylum)) %>%
    pull()

phylum_summary %>%
    left_join(genome_metadata_all %>% select(phylum) %>% unique(),by="phylum") %>%
    left_join(sample_metadata_all,by=join_by(sample==sample)) %>%
    filter(phylum != "p__") %>% 
    #mutate(phylum= sub("^p__", "", phylum)) %>%
    filter(phylum %in% phylum_arrange[1:20]) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum, fill=phylum)) +
        scale_color_manual(values=phylum_colors_all) +
        scale_fill_manual(values=phylum_colors_all) +
        geom_jitter(alpha=0.5) + 
        facet_nested(. ~ species)+
        theme_minimal() +
        theme(legend.position = "none")

4.2.2 Order

order_summary <- genome_counts_filt_all %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata_all %>% rename(host_order=order), by = join_by(sample == sample)) %>% #append sample metadata
  left_join(genome_metadata_all, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,order) %>%
  summarise(relabun=sum(count))

order_summary %>%
    filter(!is.na(relabun)) %>%
    group_by(order) %>%
    summarise(mean=mean(relabun),sd=sd(relabun)) %>%
    mutate(famorderily= sub("^o__", "", order)) %>%
    arrange(-mean) %>%
    tt()
order mean sd famorderily
o__Bacteroidales 3.795311e-01 0.1493442024 Bacteroidales
o__Lachnospirales 2.817732e-01 0.1573578010 Lachnospirales
o__Oscillospirales 7.730821e-02 0.0482442135 Oscillospirales
o__Christensenellales 6.152228e-02 0.0521531204 Christensenellales
o__Erysipelotrichales 3.205759e-02 0.0472082321 Erysipelotrichales
o__Desulfovibrionales 1.762937e-02 0.0168493333 Desulfovibrionales
o__Enterobacterales 1.596443e-02 0.0397310018 Enterobacterales
o__Campylobacterales 1.394838e-02 0.0229133933 Campylobacterales
o__Mycoplasmatales 9.674457e-03 0.0201025198 Mycoplasmatales
o__Acholeplasmatales 9.244018e-03 0.0156078657 Acholeplasmatales
o__Gastranaerophilales 8.474584e-03 0.0120549930 Gastranaerophilales
o__RF32 7.907507e-03 0.0109564629 RF32
o__RF39 7.616380e-03 0.0129867266 RF39
o__Verrucomicrobiales 7.557137e-03 0.0136890242 Verrucomicrobiales
o__Enterobacterales_A 6.146616e-03 0.0225932402 Enterobacterales_A
o__RFN20 5.491908e-03 0.0134551586 RFN20
o__TANB77 5.387227e-03 0.0091071005 TANB77
o__Peptostreptococcales 4.806343e-03 0.0075471530 Peptostreptococcales
o__UBA3830 4.368400e-03 0.0098939122 UBA3830
o__Clostridiales 4.071744e-03 0.0181994301 Clostridiales
o__Selenomonadales 3.953551e-03 0.0066974902 Selenomonadales
o__Peptococcales 3.689298e-03 0.0055754286 Peptococcales
o__Sphaerochaetales 3.529054e-03 0.0216011752 Sphaerochaetales
o__Diplorickettsiales 3.505108e-03 0.0459055178 Diplorickettsiales
o__Fusobacteriales 3.024136e-03 0.0134489815 Fusobacteriales
o__Acidaminococcales 2.551799e-03 0.0049055131 Acidaminococcales
o__Methanosarcinales 2.372541e-03 0.0097274543 Methanosarcinales
o__RUG11792 2.184233e-03 0.0053494956 RUG11792
o__Actinomycetales 1.561179e-03 0.0190765399 Actinomycetales
o__Lactobacillales 1.265853e-03 0.0051617233 Lactobacillales
o__Chlamydiales 1.109162e-03 0.0112564276 Chlamydiales
o__Elusimicrobiales 1.104802e-03 0.0036746035 Elusimicrobiales
o__ML615J-28 9.826848e-04 0.0037803511 ML615J-28
o__Brevinematales 8.957611e-04 0.0046437768 Brevinematales
o__GWE2-31-10 8.620607e-04 0.0040717621 GWE2-31-10
o__Coriobacteriales 6.601149e-04 0.0013420095 Coriobacteriales
o__CAJFEE01 6.063947e-04 0.0028904690 CAJFEE01
o__Methanomicrobiales 5.754579e-04 0.0026410958 Methanomicrobiales
o__ 5.717583e-04 0.0021241114
o__Rs-D84 5.603757e-04 0.0031715201 Rs-D84
o__Rickettsiales 5.287290e-04 0.0027549629 Rickettsiales
o__UBA1381 5.035832e-04 0.0015475012 UBA1381
o__Opitutales 4.901270e-04 0.0018532230 Opitutales
o__Synergistales 3.666072e-04 0.0024311729 Synergistales
o__Pseudomonadales 3.626249e-04 0.0029135461 Pseudomonadales
o__UBA7702 2.450743e-04 0.0015747068 UBA7702
o__Cardiobacteriales 2.276453e-04 0.0029855408 Cardiobacteriales
o__Pirellulales 1.908305e-04 0.0017144286 Pirellulales
o__Victivallales 1.789455e-04 0.0015054509 Victivallales
o__UBA4068 1.769355e-04 0.0005665926 UBA4068
o__Burkholderiales 1.721286e-04 0.0007779434 Burkholderiales
o__Treponematales 1.581747e-04 0.0008279556 Treponematales
o__Deferribacterales 1.413909e-04 0.0006340007 Deferribacterales
o__UMGS1883 7.335614e-05 0.0004670412 UMGS1883
o__Methanomassiliicoccales 4.851518e-05 0.0006362706 Methanomassiliicoccales
o__Eubacteriales 3.408400e-05 0.0001552477 Eubacteriales
o__Rhodobacterales 2.776073e-05 0.0003101775 Rhodobacterales
o__Tissierellales 2.729052e-05 0.0003579118 Tissierellales
order_arrange <- order_summary %>%
    filter(!is.na(relabun)) %>%
    group_by(order) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(order) %>%
    mutate(order= sub("^o__", "", order)) %>%
    pull()

order_summary %>%
    left_join(genome_metadata_all %>% select(order,phylum) %>% unique(),by="order") %>%
    left_join(sample_metadata_all %>% rename(host_order=order),by="sample") %>%
    filter(order != "o__") %>% 
    mutate(order= sub("^o__", "", order)) %>%
    filter(order %in% order_arrange[1:20]) %>%
    mutate(order=factor(order,levels=rev(order_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=order, group=order, color=phylum, fill=phylum)) +
        scale_color_manual(values=phylum_colors_all) +
        scale_fill_manual(values=phylum_colors_all) +
        #geom_boxplot(alpha=0.2) +
        geom_jitter(alpha=0.5) + 
        facet_nested(. ~ species)+
        theme_minimal() +
        theme(legend.position = "none")

4.2.3 Family

family_summary <- genome_counts_filt_all %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata_all, by = join_by(sample == sample)) %>% #append sample metadata
  left_join(genome_metadata_all, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,family) %>%
  summarise(relabun=sum(count))

family_summary %>%
    filter(!is.na(relabun)) %>%
    group_by(family) %>%
    summarise(mean=mean(relabun),sd=sd(relabun)) %>%
    mutate(family= sub("^f__", "", family)) %>%
    arrange(-mean) %>%
    tt()
family mean sd
Lachnospiraceae 2.679049e-01 1.479098e-01
Bacteroidaceae 2.128971e-01 1.133002e-01
Tannerellaceae 7.843675e-02 4.086968e-02
Rikenellaceae 4.521289e-02 3.594946e-02
Marinifilaceae 3.815762e-02 3.130093e-02
Ruminococcaceae 3.516717e-02 2.681469e-02
UBA3700 3.100225e-02 3.669198e-02
Oscillospiraceae 2.542478e-02 2.063380e-02
Erysipelotrichaceae 2.220306e-02 3.495105e-02
1.854607e-02 2.537000e-02
Desulfovibrionaceae 1.762937e-02 1.684933e-02
Enterobacteriaceae 1.596443e-02 3.973100e-02
Helicobacteraceae 1.394838e-02 2.291339e-02
Acutalibacteraceae 1.129138e-02 2.182770e-02
Coprobacillaceae 9.854533e-03 1.610175e-02
Mycoplasmoidaceae 9.198005e-03 1.988191e-02
Pumilibacteraceae 8.303712e-03 2.717791e-02
UBA660 7.616380e-03 1.298673e-02
Akkermansiaceae 7.557137e-03 1.368902e-02
CAG-239 7.371042e-03 9.989587e-03
Gastranaerophilaceae 7.216239e-03 1.061888e-02
Anaerotignaceae 6.013824e-03 8.783101e-03
Anaeroplasmataceae 5.687338e-03 1.422618e-02
Butyricicoccaceae 5.289808e-03 6.576968e-03
CAG-74 4.607693e-03 1.032186e-02
CAG-508 4.495099e-03 8.141235e-03
Anaerovoracaceae 4.441054e-03 7.271621e-03
Vibrionaceae 4.421724e-03 1.848524e-02
Clostridiaceae 4.071744e-03 1.819943e-02
DTU072 3.904841e-03 7.233438e-03
Peptococcaceae 3.689298e-03 5.575429e-03
CAG-449 3.598003e-03 1.246597e-02
Sphaerochaetaceae 3.529054e-03 2.160118e-02
WRBM01 3.523614e-03 6.291393e-03
Diplorickettsiaceae 3.505108e-03 4.590552e-02
UBA932 3.368690e-03 8.602302e-03
Fusobacteriaceae 3.024136e-03 1.344898e-02
CAG-917 2.786813e-03 1.051273e-02
Acidaminococcaceae 2.551799e-03 4.905513e-03
Methanosarcinaceae 2.372541e-03 9.727454e-03
Borkfalkiaceae 2.215945e-03 9.726245e-03
UBA1242 1.975129e-03 4.034220e-03
CAG-274 1.901813e-03 4.757881e-03
Aeromonadaceae 1.724892e-03 1.089339e-02
CAG-288 1.623242e-03 5.036122e-03
Micrococcaceae 1.546507e-03 1.907650e-02
MGBC116941 1.403668e-03 4.908522e-03
CALTSX01 1.109162e-03 1.125643e-02
Elusimicrobiaceae 1.104802e-03 3.674604e-03
CAG-314 1.067128e-03 3.487523e-03
CAG-138 1.028579e-03 6.324693e-03
CALVMC01 9.864994e-04 3.349719e-03
RUG14156 9.300233e-04 2.736356e-03
UBA3830 9.142222e-04 2.758943e-03
Brevinemataceae 8.957611e-04 4.643777e-03
GWE2-31-10 8.620607e-04 4.071762e-03
Eggerthellaceae 6.601149e-04 1.342009e-03
CAJFEE01 6.063947e-04 2.890469e-03
Methanocorpusculaceae 5.754579e-04 2.641096e-03
Rs-D84 5.603757e-04 3.171520e-03
Muribaculaceae 5.226674e-04 3.051788e-03
CAG-313 5.117558e-04 2.193878e-03
UBA1381 5.035832e-04 1.547501e-03
LL51 4.763559e-04 1.852608e-03
CAG-698 4.709290e-04 2.164469e-03
UBA1997 4.208195e-04 2.382923e-03
Streptococcaceae 4.160441e-04 2.630200e-03
Enterococcaceae 4.090411e-04 1.517990e-03
CAG-465 3.788372e-04 1.164817e-03
Coprobacteraceae 3.767262e-04 1.347094e-03
Synergistaceae 3.666072e-04 2.431173e-03
Peptostreptococcaceae 3.652882e-04 1.348669e-03
Pseudomonadaceae 3.626249e-04 2.913546e-03
Massilibacillaceae 3.253008e-04 1.474960e-03
UBA3637 3.139388e-04 1.531614e-03
Metamycoplasmataceae 3.110466e-04 3.810180e-03
CHK158-818 3.095362e-04 9.740463e-04
CAG-631 2.706635e-04 1.015132e-03
Lactobacillaceae 2.576707e-04 2.262342e-03
UBA7702 2.450743e-04 1.574707e-03
Wohlfahrtiimonadaceae 2.276453e-04 2.985541e-03
CAG-977 2.225261e-04 1.355731e-03
SIG350 2.069507e-04 2.714132e-03
Thermoguttaceae 1.908305e-04 1.714429e-03
CAJFVJ01 1.892159e-04 1.389700e-03
Burkholderiaceae 1.721286e-04 7.779434e-04
Hepatoplasmataceae 1.654048e-04 1.179092e-03
Treponemataceae 1.581747e-04 8.279556e-04
UBA5755 1.525827e-04 5.351000e-04
Mucispirillaceae 1.413909e-04 6.340007e-04
UBA1234 1.381659e-04 5.154643e-04
Catellicoccaceae 9.608682e-05 8.591908e-04
CAG-272 9.588815e-05 7.206419e-04
Vagococcaceae 8.701072e-05 1.141135e-03
Victivallaceae 7.718385e-05 4.488404e-04
Azobacteroidaceae 7.440778e-05 9.758489e-04
UMGS1883 7.335614e-05 4.670412e-04
Cellulosilyticaceae 5.332328e-05 3.808652e-04
Methanomethylophilaceae 4.851518e-05 6.362706e-04
DUVY01 4.637858e-05 5.673763e-04
Paludibacteraceae 4.504653e-05 2.212459e-04
CAG-382 3.918633e-05 1.936551e-04
Eubacteriaceae 3.408400e-05 1.552477e-04
JAEDCM01 3.306564e-05 4.336518e-04
Rhodobacteraceae 2.776073e-05 3.101775e-04
Peptoniphilaceae 2.729052e-05 3.579118e-04
HGM11417 2.626128e-05 1.876693e-04
UBA4068 2.435275e-05 9.859687e-05
Microbacteriaceae 1.467237e-05 1.924263e-04
UBA9783 1.377110e-05 1.243972e-04
WCHB1-69 5.663380e-06 7.427454e-05
family_arrange <- family_summary %>%
    filter(!is.na(relabun)) %>%
    group_by(family) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(family) %>%
    mutate(family= sub("^f__", "", family)) %>%
    pull()

family_summary %>%
    left_join(genome_metadata_all %>% select(family,phylum) %>% unique(),by=join_by(family==family)) %>%
    left_join(sample_metadata_all,by=join_by(sample==sample)) %>%
    filter(family != "f__") %>% 
    mutate(family= sub("^f__", "", family)) %>%
    filter(family %in% family_arrange[1:20]) %>%
    mutate(family=factor(family,levels=rev(family_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=family, group=family, color=phylum, fill=phylum)) +
        scale_color_manual(values=phylum_colors_all) +
        scale_fill_manual(values=phylum_colors_all) +
        #geom_boxplot(alpha=0.2) +
        geom_jitter(alpha=0.5) + 
        facet_nested(. ~ species)+
        theme_minimal() +
        theme(legend.position = "none")

4.2.4 Genus

genus_summary <- genome_counts_filt_all %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata_all, by = join_by(sample == sample)) %>% #append sample metadata
  left_join(genome_metadata_all, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,genus) %>%
  summarise(relabun=sum(count)) %>%
  filter(genus != "g__")

genus_summary %>%
    filter(!is.na(relabun)) %>%
    group_by(genus) %>%
    summarise(mean=mean(relabun),sd=sd(relabun)) %>%
    arrange(-mean) %>%
    tt()
genus mean sd
g__Bacteroides 1.492633e-01 9.145770e-02
g__Parabacteroides 6.838309e-02 3.825177e-02
g__Phocaeicola 5.590755e-02 4.767695e-02
g__Odoribacter 3.698228e-02 3.157692e-02
g__Alistipes 3.688633e-02 3.262665e-02
g__Kineothrix 3.485495e-02 7.746321e-02
g__JAAYNV01 3.180220e-02 5.544362e-02
g__Roseburia 2.034157e-02 6.055895e-02
g__Acetatifactor 1.353817e-02 2.662322e-02
g__Ventrimonas 1.119879e-02 1.784188e-02
g__CAG-95 1.116264e-02 2.245897e-02
g__Helicobacter_J 1.041623e-02 1.995870e-02
g__Parabacteroides_B 9.901565e-03 1.173939e-02
g__Breznakia 9.810792e-03 2.108634e-02
g__Mycoplasmoides 9.198005e-03 1.988191e-02
g__Akkermansia 7.557137e-03 1.368902e-02
g__Velocimicrobium 7.454088e-03 1.907730e-02
g__RGIG4733 7.157888e-03 2.514900e-02
g__Hungatella_A 6.350838e-03 9.898917e-03
g__UBA866 5.974162e-03 1.279000e-02
g__Desulfovibrio 5.709603e-03 9.315901e-03
g__Anaerotruncus 5.630278e-03 7.811119e-03
g__Thomasclavelia 5.493662e-03 1.248939e-02
g__Intestinimonas 5.301231e-03 6.478704e-03
g__Bilophila 5.134848e-03 6.647822e-03
g__Ruthenibacterium 4.907256e-03 9.923689e-03
g__Enterocloster 4.615640e-03 6.281140e-03
g__JALFVM01 4.503361e-03 1.060918e-02
g__Vibrio 4.421724e-03 1.848524e-02
g__Hespellia 4.402515e-03 1.129796e-02
g__Fimenecus 4.242280e-03 1.435771e-02
g__14-2 3.563480e-03 1.647062e-02
g__Clostridium_Q 3.535110e-03 4.975653e-03
g__NHYM01 3.532146e-03 9.913909e-03
g__Spiro-02 3.529054e-03 2.160118e-02
g__Aquirickettsiella 3.505108e-03 4.590552e-02
g__Copromonas 3.447747e-03 4.581581e-03
g__Lawsonibacter 3.443311e-03 5.211912e-03
g__Egerieousia 3.368690e-03 8.602302e-03
g__Protoclostridium 3.296536e-03 2.553531e-02
g__Enterobacter 3.191565e-03 1.402631e-02
g__Lacrimispora 3.189242e-03 7.287909e-03
g__Oscillibacter 3.157271e-03 4.419607e-03
g__CHH4-2 3.075367e-03 5.253794e-03
g__Escherichia 3.000053e-03 1.115703e-02
g__Limiplasma 2.969019e-03 7.133192e-03
g__Rikenella 2.939128e-03 6.055218e-03
g__Sarcina 2.877939e-03 1.630802e-02
g__CAZU01 2.870401e-03 4.887287e-03
g__Fusobacterium_A 2.859566e-03 1.339771e-02
g__Dielma 2.815223e-03 6.874781e-03
g__OM05-12 2.791990e-03 5.749748e-03
g__Phascolarctobacterium 2.551799e-03 4.905513e-03
g__Fimivivens 2.467945e-03 3.688432e-03
g__MGBC136627 2.446550e-03 6.005822e-03
g__MGBC143606 2.440758e-03 7.414283e-03
g__Methanimicrococcus 2.372541e-03 9.727454e-03
g__Intestinibacillus 2.365302e-03 5.280365e-03
g__Hungatella 2.292660e-03 4.692286e-03
g__Mailhella 2.254541e-03 3.960166e-03
g__Pseudoflavonifractor 2.140159e-03 5.519017e-03
g__Gemmiger 2.112060e-03 6.521526e-03
g__MGBC140009 2.031711e-03 5.716638e-03
g__Lachnotalea 2.012696e-03 5.442239e-03
g__Pelethenecus 2.008513e-03 8.013717e-03
g__WRDF01 1.983926e-03 3.923816e-03
g__MGBC131033 1.973766e-03 3.165900e-03
g__Dysosmobacter 1.947698e-03 2.768625e-03
g__Negativibacillus 1.942876e-03 4.332910e-03
g__Coprobacillus 1.934097e-03 3.859945e-03
g__Angelakisella 1.884111e-03 3.631754e-03
g__RGIG6463 1.836103e-03 3.631321e-03
g__MGBC165282 1.805128e-03 4.854526e-03
g__Buttiauxella 1.783363e-03 6.842193e-03
g__JAAYQI01 1.771220e-03 3.100609e-03
g__Limenecus 1.757957e-03 3.763048e-03
g__Aeromonas 1.724892e-03 1.089339e-02
g__Agathobaculum 1.691308e-03 3.310944e-03
g__Citrobacter 1.678009e-03 7.050371e-03
g__Coproplasma 1.676723e-03 9.512497e-03
g__SZUA-378 1.657792e-03 1.399271e-02
g__Bacteroides_E 1.646079e-03 2.158813e-02
g__Eisenbergiella 1.567253e-03 3.864070e-03
g__Acaricomes 1.546507e-03 1.907650e-02
g__SIG332 1.531952e-03 9.459875e-03
g__CAG-269 1.410352e-03 3.725174e-03
g__SIG299 1.380919e-03 7.119758e-03
g__NSJ-61 1.352789e-03 4.082352e-03
g__RGIG3002 1.316711e-03 4.431909e-03
g__Marseille-P3106 1.288557e-03 2.227308e-03
g__WRHT01 1.286176e-03 3.892975e-03
g__Kosakonia 1.279803e-03 1.185315e-02
g__Emergencia 1.251317e-03 4.473343e-03
g__UMGS1251 1.238852e-03 3.182570e-03
g__Tidjanibacter 1.233090e-03 3.465801e-03
g__C-19 1.222456e-03 4.824577e-03
g__Clostridium 1.193805e-03 7.940942e-03
g__Butyricimonas 1.175332e-03 2.982029e-03
g__MGBC116941 1.143994e-03 4.759873e-03
g__Anaerotignum 1.131763e-03 1.921774e-03
g__Evtepia 1.125245e-03 2.346285e-03
g__Scatousia 1.113860e-03 3.186063e-03
g__Scandinavium 1.110443e-03 1.289658e-02
g__CALTSX01 1.109162e-03 1.125643e-02
g__Anaeroplasma 1.099325e-03 4.010838e-03
g__UBA2658 1.093757e-03 2.645590e-03
g__Blautia_A 1.062518e-03 2.897013e-03
g__Proteus 1.058683e-03 1.335374e-02
g__Ruminococcus_E 1.029855e-03 6.336954e-03
g__JAHHSE01 1.016881e-03 2.081230e-03
g__Anaerostipes 9.993568e-04 6.214220e-03
g__CAJLXD01 9.907104e-04 2.932215e-03
g__Phocaeicola_A 9.892770e-04 4.013536e-03
g__Fournierella 9.581274e-04 2.619171e-03
g__CAG-345 9.417628e-04 4.156477e-03
g__Brevinema 8.957611e-04 4.643777e-03
g__Harryflintia 8.884142e-04 2.505438e-03
g__C-53 8.874160e-04 4.469056e-03
g__Acutalibacter 8.861698e-04 1.776887e-03
g__Aminipila 8.810006e-04 2.463410e-03
g__Spyradomonas 8.525628e-04 4.191135e-03
g__Ventrenecus 8.497155e-04 2.794178e-03
g__CAG-273 8.444700e-04 4.049118e-03
g__Caccovivens 8.366088e-04 2.621483e-03
g__VSOB01 8.152912e-04 3.184675e-03
g__CAG-1782 7.959184e-04 3.106148e-03
g__RGIG1896 7.925359e-04 1.323233e-03
g__Ruminiclostridium_E 7.797273e-04 3.917287e-03
g__CAG-582 7.735000e-04 4.481513e-03
g__Blautia 7.711825e-04 1.670642e-03
g__Klebsiella 7.596956e-04 3.591632e-03
g__Bariatricus 7.585807e-04 1.661520e-03
g__Extibacter 7.391720e-04 1.570615e-03
g__Stoquefichus 7.391191e-04 3.131298e-03
g__CALURL01 7.372695e-04 2.459028e-03
g__JAAWBF01 7.270628e-04 2.291304e-03
g__UBA1436 7.244074e-04 2.911088e-03
g__UBA5578 6.989678e-04 4.423923e-03
g__IOR16 6.837972e-04 1.623524e-03
g__CAG-288 6.814792e-04 2.646198e-03
g__Clostridium_AI 6.796597e-04 7.646021e-03
g__MGBC133411 6.731026e-04 1.995666e-03
g__CALUVN01 6.547298e-04 1.647970e-03
g__Scatenecus 6.527575e-04 2.463681e-03
g__UBA7185 6.237137e-04 2.850324e-03
g__Faecousia 6.199737e-04 2.757282e-03
g__Muricomes 6.193494e-04 2.526333e-03
g__Scatacola_A 6.113889e-04 2.209083e-03
g__Faecalibacillus 6.073162e-04 3.608509e-03
g__Harrysmithimonas 6.063947e-04 2.890469e-03
g__CAJTFG01 5.848933e-04 3.604164e-03
g__Bacilliculturomica 5.704876e-04 1.402287e-03
g__Faecisoma 5.592534e-04 2.521527e-03
g__Methanocorpusculum 5.525498e-04 2.600043e-03
g__Anaerocaecibacter 5.522731e-04 2.721369e-03
g__CAKQDS01 5.418129e-04 2.199150e-03
g__Salmonella 5.276877e-04 4.414352e-03
g__RGIG8482 5.219404e-04 1.949521e-03
g__Galloscillospira_A 5.158552e-04 9.715988e-04
g__Fimivicinus 5.153979e-04 2.165963e-03
g__Fimimorpha 4.980684e-04 2.338325e-03
g__JAGAJR01 4.971561e-04 3.285758e-03
g__Caccenecus 4.949970e-04 1.527726e-03
g__UBA1794 4.933027e-04 1.707288e-03
g__UBA1417 4.791205e-04 1.029684e-03
g__UBA940 4.740035e-04 1.712347e-03
g__CAJFPI01 4.629136e-04 1.001336e-03
g__Lactonifactor 4.603409e-04 1.417494e-03
g__Clostridium_AQ 4.466972e-04 1.736532e-03
g__Onthovicinus 4.445381e-04 2.331257e-03
g__Buchnera 4.433102e-04 5.813959e-03
g__Clostridium_N 4.402910e-04 2.950622e-03
g__CAKRHR01 4.367869e-04 2.278772e-03
g__DXYV01 4.360731e-04 1.398616e-03
g__RGIG1902 4.283551e-04 7.371513e-04
g__SIG32 4.227733e-04 1.529877e-03
g__HGM11386 4.218531e-04 1.727808e-03
g__Butyrivibrio_A 4.136198e-04 3.448206e-03
g__FLUQ01 4.120756e-04 9.918380e-04
g__CAG-590 4.073011e-04 1.856155e-03
g__Faecimonas 4.024457e-04 1.952064e-03
g__MGBC107952 4.009827e-04 1.132923e-03
g__Ventrisoma 3.996970e-04 1.047519e-03
g__Beduini 3.891743e-04 1.496954e-03
g__RUG14156 3.667219e-04 1.782969e-03
g__Cloacibacillus 3.666072e-04 2.431173e-03
g__Pseudomonas 3.626249e-04 2.913546e-03
g__Lawsonia 3.609631e-04 2.976114e-03
g__Marvinbryantia 3.602193e-04 1.589621e-03
g__CAG-465 3.584980e-04 1.153031e-03
g__Coprobacter 3.568144e-04 1.332767e-03
g__HGM12998 3.510965e-04 1.268564e-03
g__Faecenecus 3.477335e-04 1.547966e-03
g__RACS-047 3.410345e-04 1.305915e-03
g__RGIG4097 3.394551e-04 2.512766e-03
g__CAG-303 3.354250e-04 2.253817e-03
g__Paraeggerthella 3.306477e-04 7.881768e-04
g__Scatavimonas 3.292069e-04 2.998578e-03
g__Scatocola 3.170079e-04 2.010498e-03
g__CAKTEE01 3.148626e-04 1.817047e-03
g__UBA7488 3.139388e-04 1.531614e-03
g__Caccomorpha 3.135442e-04 8.132352e-04
g__UBA710 3.110466e-04 3.810180e-03
g__Gallibacteroides 3.095362e-04 9.740463e-04
g__Lachnospira 3.070567e-04 1.867816e-03
g__CALXUC01 3.067121e-04 1.882934e-03
g__JAIHAL01 2.938058e-04 1.765062e-03
g__Zag111 2.895640e-04 1.695054e-03
g__Ventricola 2.878743e-04 1.507071e-03
g__Scatomorpha 2.765635e-04 7.278705e-04
g__Lachnoclostridium_B 2.763740e-04 8.906298e-04
g__UBA5026 2.752265e-04 1.128502e-03
g__Caccosoma 2.706635e-04 1.015132e-03
g__Catenibacillus 2.681525e-04 7.308965e-04
g__JAGBWK01 2.667486e-04 2.985884e-03
g__RGIG4057 2.634560e-04 1.610134e-03
g__Oliverpabstia 2.606026e-04 2.224448e-03
g__UMGS1202 2.590747e-04 8.409060e-04
g__HGM05232 2.559188e-04 7.315989e-04
g__Merdicola 2.528427e-04 1.910082e-03
g__Cryptoclostridium 2.450743e-04 1.574707e-03
g__UMGS2016 2.439149e-04 1.057064e-03
g__CALXWF01 2.437349e-04 8.980698e-04
g__Onthocola_B 2.336122e-04 1.097069e-03
g__RUG14670 2.334104e-04 1.268072e-03
g__UMGS1663 2.320796e-04 1.174466e-03
g__Plesiomonas 2.292023e-04 2.122607e-03
g__SIG230 2.280915e-04 9.725989e-04
g__Butyricicoccus 2.280100e-04 1.179904e-03
g__Butyribacter 2.278963e-04 1.554091e-03
g__HGM13233 2.269566e-04 1.044833e-03
g__JAHHUA01 2.254978e-04 1.219591e-03
g__CALWZU01 2.244363e-04 7.795409e-04
g__CAG-977 2.225261e-04 1.355731e-03
g__Aphodocola 2.222897e-04 1.161474e-03
g__CALXRO01 2.218957e-04 1.970482e-03
g__Heteroruminococcus 2.202694e-04 9.244284e-04
g__JAJQAW01 2.169416e-04 7.681528e-04
g__Onthenecus 2.147993e-04 1.115093e-03
g__RGIG7389 2.135590e-04 6.700920e-04
g__Faecivivens 2.124632e-04 6.117535e-04
g__Onthousia 2.092884e-04 1.156334e-03
g__SIG350 2.069507e-04 2.714132e-03
g__Zhenpiania 2.019229e-04 4.491625e-04
g__WQYD01 2.010363e-04 7.107867e-04
g__Schmidhempelia 1.993159e-04 2.428842e-03
g__CAJKWP01 1.983903e-04 1.179880e-03
g__Enterococcus_B 1.954034e-04 9.681045e-04
g__JAILHT01 1.916321e-04 1.904125e-03
g__RGIG3701 1.908305e-04 1.714429e-03
g__Ignatzschineria 1.894434e-04 2.484527e-03
g__Avoscillospira_A 1.849958e-04 7.621367e-04
g__RUG12867 1.830035e-04 9.744369e-04
g__CAG-245 1.810191e-04 1.094579e-03
g__Anaerovorax 1.807911e-04 8.302159e-04
g__Raoultibacter 1.797664e-04 8.292516e-04
g__UMGS1585 1.752553e-04 7.702768e-04
g__Oxalobacter 1.721286e-04 7.779434e-04
g__Hafnia 1.709747e-04 1.378637e-03
g__CAG-510 1.665191e-04 8.640457e-04
g__Kluyvera 1.663693e-04 1.308586e-03
g__Hepatoplasma 1.654048e-04 1.179092e-03
g__UMGS75 1.626765e-04 1.435280e-03
g__JADFUS01 1.619273e-04 6.573895e-04
g__UBA7405 1.612771e-04 2.016590e-03
g__Streptococcus 1.564224e-04 2.051461e-03
g__CAG-56 1.525484e-04 6.872474e-04
g__CCUG-7971 1.523791e-04 6.252194e-04
g__Barb7 1.520997e-04 1.994769e-03
g__Gallispira 1.502610e-04 1.952849e-03
g__Lapidilactobacillus 1.409595e-04 1.848667e-03
g__Butyricicoccus_A 1.367025e-04 7.053366e-04
g__Limousia 1.360041e-04 6.955315e-04
g__Galligastranaerophilus 1.353461e-04 7.910321e-04
g__RGIG8607 1.330395e-04 8.153357e-04
g__Romboutsia_C 1.324145e-04 8.281891e-04
g__Enterococcus 1.324071e-04 8.356372e-04
g__Lactococcus_A 1.314638e-04 1.483600e-03
g__Leclercia 1.312203e-04 8.697175e-04
g__Pelethosoma 1.307797e-04 6.745119e-04
g__Lactococcus 1.281579e-04 7.899264e-04
g__MGBC124762 1.275812e-04 3.998533e-04
g__MGBC102946 1.258646e-04 5.024944e-04
g__CALXDZ01 1.242807e-04 4.083501e-04
g__Alectryocaccomicrobium 1.235486e-04 7.824254e-04
g__Avelusimicrobium 1.231087e-04 5.021372e-04
g__Murimonas 1.213808e-04 6.752085e-04
g__CALXIC01 1.201073e-04 4.762615e-04
g__Eubacterium_R 1.155168e-04 6.661566e-04
g__CAJMNU01 1.144585e-04 4.126897e-04
g__Metalachnospira 1.123175e-04 3.821465e-04
g__MGBC162267 1.119052e-04 5.900730e-04
g__JAEXFV01 1.108700e-04 5.225033e-04
g__UBA4636 1.101747e-04 5.696311e-04
g__Ruminococcus_G 1.085800e-04 5.727591e-04
g__JAHZFN01 1.041903e-04 4.947845e-04
g__Alistipes_A 1.023731e-04 3.424616e-04
g__Adamsella 1.005056e-04 8.259359e-04
g__JAAVYW01 9.886611e-05 3.596087e-04
g__UBA6345 9.776278e-05 5.890564e-04
g__UMGS1601 9.678895e-05 1.027188e-03
g__UMGS946 9.645721e-05 7.355573e-04
g__Catellicoccus 9.608682e-05 8.591908e-04
g__Novisyntrophococcus 9.472371e-05 6.442503e-04
g__CAG-41 9.123793e-05 6.894142e-04
g__CAG-307 9.056080e-05 6.397842e-04
g__CAKPCJ01 8.942488e-05 4.738890e-04
g__JAEWLZ01 8.871037e-05 1.125625e-03
g__Scybalousia 8.811278e-05 6.607347e-04
g__JAGZHZ01 8.729754e-05 5.699666e-04
g__Vagococcus 8.701072e-05 1.141135e-03
g__CAG-177 8.598892e-05 6.008884e-04
g__JALENY01 8.400275e-05 7.456506e-04
g__UBA1234 8.317669e-05 3.789843e-04
g__Eggerthella 8.252648e-05 3.589205e-04
g__Paraclostridium 8.049454e-05 6.908705e-04
g__UBA9414 7.948449e-05 4.143991e-04
g__Howiella 7.930957e-05 4.148145e-04
g__WRAV01 7.609451e-05 3.151892e-04
g__1XD8-76 7.561457e-05 5.115263e-04
g__Coprosoma 7.472227e-05 3.452372e-04
g__Symbiothrix 7.440778e-05 9.758489e-04
g__Eubacterium_F 7.419399e-05 4.473563e-04
g__CALVUN01 7.360091e-05 2.973254e-04
g__SIG471 7.262934e-05 2.760627e-04
g__PeH17 7.223390e-05 6.588230e-04
g__CALVXC01 7.196506e-05 6.695435e-04
g__RGIG4709 6.834702e-05 3.413827e-04
g__NSJ-51 6.807371e-05 2.871386e-04
g__Stercorousia 6.781125e-05 5.448313e-04
g__Gordonibacter 6.717435e-05 2.612900e-04
g__MGBC114844 6.659331e-05 4.105335e-04
g__Pelethomonas 6.618416e-05 2.448069e-04
g__Victivallis 6.489695e-05 4.051890e-04
g__RGIG9115 6.478642e-05 4.986757e-04
g__UBA1405 6.337712e-05 3.647934e-04
g__MGBC164599 6.309143e-05 2.415782e-04
g__UMGS1754 6.307099e-05 4.707822e-04
g__CAG-196 6.303657e-05 3.691902e-04
g__Fructobacillus 6.009139e-05 7.880912e-04
g__Anaeromassilibacillus 5.991756e-05 4.100453e-04
g__MGBC120314 5.725195e-05 5.476452e-04
g__JAAZGC01 5.557462e-05 5.355315e-04
g__CAG-314 5.351060e-05 4.224425e-04
g__Cellulosilyticum 5.332328e-05 3.808652e-04
g__Enterousia 5.188415e-05 4.191287e-04
g__UMGS687 5.126641e-05 4.219539e-04
g__Erysipelothrix 5.024616e-05 6.589722e-04
g__Holdemania 4.990583e-05 2.392481e-04
g__Limivicinus 4.957042e-05 6.142188e-04
g__RGIG4790 4.884680e-05 4.322050e-04
g__Enterococcus_D 4.788989e-05 4.103756e-04
g__UBA933 4.778105e-05 6.266426e-04
g__Copranaerobaculum 4.777314e-05 2.215807e-04
g__SIG603 4.629171e-05 3.498294e-04
g__CAKVBE01 4.606653e-05 6.041568e-04
g__Mobilisporobacter 4.560952e-05 4.555415e-04
g__QVMH01 4.504653e-05 2.212459e-04
g__Hydrogenoanaerobacterium 4.475602e-05 1.749987e-04
g__CALWRB01 4.428930e-05 1.565875e-04
g__HGM16780 4.234877e-05 3.457660e-04
g__JAFSEX01 4.208347e-05 5.006300e-04
g__CALWTV01 4.031353e-05 4.868653e-04
g__Ruminococcus 4.026239e-05 5.280363e-04
g__Providencia 3.947092e-05 4.865493e-04
g__Woodwardibium 3.918633e-05 1.936551e-04
g__Frigididesulfovibrio 3.840477e-05 5.036739e-04
g__Wohlfahrtiimonas 3.820192e-05 5.010134e-04
g__Eubacterium 3.408400e-05 1.552477e-04
g__Morganella 3.398929e-05 4.457653e-04
g__Enterococcus_J 3.334065e-05 4.372585e-04
g__Neoruminococcus 3.310136e-05 1.689688e-04
g__RUG13868 3.198945e-05 2.291845e-04
g__UBA1394 3.162426e-05 2.289144e-04
g__JAAZDN01 3.052698e-05 4.003575e-04
g__CAG-452 2.832554e-05 1.923112e-04
g__Paracoccus 2.776073e-05 3.101775e-04
g__JAILQS01 2.736465e-05 1.827364e-04
g__CAKNOI01 2.732478e-05 3.262900e-04
g__RGIG2219 2.626128e-05 1.876693e-04
g__CAJOJR01 2.582848e-05 2.076085e-04
g__Ventrousia 2.576615e-05 1.230955e-04
g__HGM13862 2.435275e-05 9.859687e-05
g__Methanoplasma 2.278412e-05 2.988110e-04
g__Heteroclostridium 2.072692e-05 1.253234e-04
g__RGIG5057 2.033912e-05 1.439685e-04
g__Heteroscilispira 2.015044e-05 1.254733e-04
g__RGIG3091 1.786547e-05 2.343035e-04
g__RGIG446 1.709062e-05 8.960743e-05
g__Lentilactobacillus 1.631166e-05 2.139254e-04
g__Microbacterium 1.467237e-05 1.924263e-04
g__Alloscillospira 1.272669e-05 1.020529e-04
g__CALXXL01 1.228690e-05 7.294195e-05
g__SIG208 6.550316e-06 5.024769e-05
g__CAKVLS01 6.485638e-06 5.776699e-05
g__JAGVVH01 5.663380e-06 7.427454e-05
genus_arrange <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=sum(relabun)) %>%
    filter(genus != "g__")%>%
    arrange(-mean) %>%
    select(genus) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    pull()

genus_summary %>%
    left_join(genome_metadata_all %>% select(genus,phylum) %>% unique(),by=join_by(genus==genus)) %>%
    left_join(sample_metadata_all,by=join_by(sample==sample)) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    filter(genus %in% genus_arrange[1:20]) %>%
    mutate(genus=factor(genus,levels=rev(genus_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=genus, group=genus, color=phylum)) +
        scale_color_manual(values=phylum_colors_all) +
        #geom_boxplot() +
        geom_jitter(alpha=0.5) + 
        facet_nested(. ~ species)+
        theme_minimal()

4.3 Compositional dissimilarities

beta_q0n <- genome_counts_filt_all %>%
  column_to_rownames(., "genome") %>%
  filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
  select_if(~!all(. == 0)) %>%
  hillpair(., q = 0)

beta_q1n <- genome_counts_filt_all %>%
  column_to_rownames(., "genome") %>%
  filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
  select_if(~!all(. == 0)) %>%
  hillpair(., q = 1)

beta_q1p <- genome_counts_filt_all %>%
  column_to_rownames(., "genome") %>%
  filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
  select_if(~!all(. == 0)) %>%
  hillpair(., q = 1, tree = genome_tree_all)

save(beta_q0n,beta_q1n,file="data/beta_all.Rdata")
load("data/beta_all.Rdata")

4.3.1 Richness dissimilarity plot

nmds_q0n <- beta_q0n$S %>%
  vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata_all, by = "sample") %>%
  group_by(species,population) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup()

nmds_q0n %>%
  ggplot(aes(x = NMDS1, y = NMDS2, color = species, fill = species, shape = population_type)) +
    geom_point(size = 2) +
    scale_color_manual(values=c("#f48153","#83d3d4","#2d8183","#910c07"))+
    #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
    geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.5) +
    theme_classic() +
    theme(
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      axis.title = element_text(size = 20, face = "bold"),
      axis.text = element_text(face = "bold", size = 18),
      panel.background = element_blank(),
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      legend.text = element_text(size = 16),
      legend.title = element_text(size = 18),
      legend.position = "right", legend.box = "vertical"
    ) +
    labs(shape="Population type",color="Species",fill="Species")

4.3.2 Neutral diversity dissimilarity plot

nmds_q1n <- beta_q1n$S %>%
  vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata_all, by = "sample") %>%
  group_by(species,population) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup()

nmds_q1n %>%
  ggplot(aes(x = NMDS1, y = NMDS2, color = species, fill = species, shape = population_type)) +
    geom_point(size = 2) +
    scale_color_manual(values=c("#f48153","#83d3d4","#2d8183","#910c07"))+
    #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
    geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.5) +
    theme_classic() +
    theme(
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      axis.title = element_text(size = 20, face = "bold"),
      axis.text = element_text(face = "bold", size = 18),
      panel.background = element_blank(),
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      legend.text = element_text(size = 16),
      legend.title = element_text(size = 18),
      legend.position = "right", legend.box = "vertical"
    ) +
    labs(shape="Population type",color="Species",fill="Species")

4.3.3 Phylogenetic diversity dissimilarity plot

nmds_q1p <- beta_q1p$S %>%
  vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  dplyr::left_join(sample_metadata_all, by = "sample") %>%
  group_by(species,population) %>%
  mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
  mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
  ungroup()

nmds_q1p %>%
  ggplot(aes(x = NMDS1, y = NMDS2, color = species, fill = species, shape = population_type)) +
    geom_point(size = 2) +
    scale_color_manual(values=c("#f48153","#83d3d4","#2d8183","#910c07"))+
    #   stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
    geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.5) +
    theme_classic() +
    theme(
      axis.text.x = element_text(size = 12),
      axis.text.y = element_text(size = 12),
      axis.title = element_text(size = 20, face = "bold"),
      axis.text = element_text(face = "bold", size = 18),
      panel.background = element_blank(),
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      legend.text = element_text(size = 16),
      legend.title = element_text(size = 18),
      legend.position = "right", legend.box = "vertical"
    ) +
    labs(shape="Population type",color="Species",fill="Species")